Key insights

• ⚙️ LLMs like Chad GPT 40 are powerful multimodal models that can understand text, images, and audio, enabling real-world applications.
• 🤖 They enable zero-shot learning but come with downsides such as being slow, expensive, and requiring deployment in the cloud.
• 🌐 Using AI at the edge allows for local processing with low latency and cost, distilling knowledge from large models into smaller ones.
• 🔊 Edge Impulse enables creating AI applications for self-contained devices with over 300,000 projects already created.
• 📹 Training a smaller model using unlabeled video data, leveraging GPT for labeling, enables the creation of a more constrained but faster model to answer specific questions.
• 🖼️ Using AI to categorize images, provide reasoning, and cluster data for quick dataset cleanup and training of smaller networks.
• 🔍 Visualization tools like Data Explorer offer insights into image clusters, facilitating efficient data management and cleanup.
• 📱 Using transfer learning with a small neural network, achieving real-time performance without cloud support, and enabling efficient deployment and scaling based on problem size.

Q&A

• What is the advantage of using a smaller backbone for running models on microcontrollers?

  Using a smaller backbone allows running models on microcontrollers, achieving lower latency and lower cost. Although there is some decrease in accuracy when knowledge from big models is distilled into small ones, the goal is to enable running specialized small models on devices in the field.

• How was a model trained on a Raspberry Pi and deployed to a mobile device for real-time object recognition achieved?

  Using transfer learning with a small neural network, the model was trained on a Raspberry Pi to detect specific objects, achieving real-time performance without cloud support. The model was then easily deployed to a phone for testing and demonstrated accurate object recognition in a home environment. This approach allows for efficient deployment and scaling based on problem size.

• How can AI be used for efficient dataset cleanup and training of smaller networks?

  AI can be used to categorize images, provide reasoning, and cluster data for quick dataset cleanup and training of smaller networks. Visualization tools like Data Explorer offer insights into image clusters, facilitating efficient data management and cleanup.

• How can unlabeled video data be utilized for training a smaller model?

  Unlabeled video data can be used to train a smaller model by leveraging GPT for labeling, enabling the creation of a more constrained but faster model that can answer specific questions.

• How does AI at the edge differ from cloud deployment for AI?

  AI at the edge allows for local processing with low latency and cost, distilling knowledge from large models into smaller ones. This enables creating AI applications for self-contained devices, with over 300,000 projects already created using platforms like Edge Impulse.

• What are the downsides of LLMs like Chad GPT 40?

  The downsides of LLMs like Chad GPT 40 include being slow, expensive, and requiring deployment in the cloud.

• What are LLMs like Chad GPT 40 capable of?

  LLMs like Chad GPT 40 are powerful multimodal models that can understand text, images, and audio, enabling real-world applications. They also enable zero-shot learning.

• 00:00 LLMs like Chad GPT 40 are powerful multimodal models that can understand text, images, and audio, enabling real-world applications. They enable zero-shot learning but come with downsides such as being slow, expensive, and requiring deployment in the cloud.
• 02:28 Using AI at the edge allows for local processing with low latency and cost, distilling knowledge from large models into smaller ones. Edge Impulse enables creating AI applications for self-contained devices with over 300,000 projects already created.
• 04:44 A discussion about training a smaller model to answer specific questions using unlabeled video data and leveraging GPT for labeling, enabling the creation of a more constrained but faster model.
• 07:07 Using AI to categorize images, provide reasoning, and cluster data for quick dataset cleanup and training of smaller networks. Visualization tools like Data Explorer offer insights into image clusters. The AI model facilitates efficient data management and cleanup.
• 09:08 Using transfer learning with a small neural network, the model was trained on a Raspberry Pi to detect specific objects, achieving real-time performance without cloud support. The model was then easily deployed to a phone for testing and demonstrated accurate object recognition in a home environment. The approach allows for efficient deployment and scaling based on problem size.
• 11:26 Using a smaller backbone allows running models on microcontrollers, achieving lower latency and lower cost. Knowledge from big models is distilled into small models with some decrease in accuracy. The goal is to enable running specialized small models on devices in the field.